Optimal Demand Response of Controllable Loads in Isolated Microgrids

74 Pages·2014·0.89 MB·English

Optimal Demand Response of
Controllable Loads in Isolated
Microgrids
by
Akash Raghurajan
A thesis
presented to the University of Waterloo
in fulfillment of the
thesis requirement for the degree of
Master of Applied Science
in
Electrical and Computer Engineering
Waterloo, Ontario, Canada, 2014
© Akash Raghurajan 2014
Author’s Declaration
I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis,
including any required final revisions, as accepted by my examiners.
I understand that my thesis may be made electronically available to the public.
ii
Abstract
The electric power industry worldwide has undergone significant changes over the last
decade. Environmental compliance and energy conservation issues have occupied the fore-
front of the new age power system which have opened the possibility of an increased
integration of Distribution Energy Resources (DER). With the presence of DERs, reliable
system operation and control has become increasingly difficult as the power flow no longer
remains unidirectional. Microgrids with their decentralized system operations offer solu-
tions to the challenges posed by this transformation. It has been generally regarded that
the key to increased operational efficiency and economy of microgrids especially under its
isolated mode of operation lies with improved customer participation in Demand Response
(DR) programs. Developing a DR scheme with a novel customer interaction inside a mi-
crogrid setup will provide a key solution that would drive the system performance to its
peak.
ThisthesisproposesamathematicalmodelofDRintegratedintothegenerationschedul-
ing problem of an isolated microgrid. Controllable demand is modeled as a function of
external parameters such as outside temperature, Time-of-Use (TOU) pricing and maxi-
mum limit on demand P through supervised learning of neural networks. An optimal
max
DR model is proposed to learn the load behavior and produce a control action on the
controllable load profile of the end users. A novel Microgrid Energy Management System
(MEMS) is proposed as the central unit of this DR model to determine the control signal
and perform a least-cost operational schedule of the microgrid. Realistic data from an
actual Energy Hub Management System (EHMS) is used to better replicate the real-world
modeling scenario. Continuing with the DR model, the effect of customer response through
energy payback model is also studied. The impact of this response on the customer load
profile and an estimate of the expected peak reduction is also presented.
The proposed model and the case studies are simulated on an CIGRE IEEE Medium
Voltage (MV) benchmark system. The system under consideration is an appropriate ap-
proximation of the actual isolated microgrid system with their dispatchable diesel genera-
tors, Energy Storage System (ESS), photovoltaic (PV) panels and wind turbines. Finally,
the results illustrating the effectiveness of the proposed DR scheme and the computa-
tional procedures are discussed. This work is concluded by exploring the possible research
directions while addressing some pertinent problems for the same.
iii
Acknowledgements
First and foremost I would like to acknowledge and thank my supervisors, Dr. Kankar
Bhattacharya and Dr. Claudio Canizares for their unwavering support and guidance
throughout my Masters. Their patience and advice has had an invaluable impact on
my student life. I will continue to draw inspiration from you. I would also like to express
my thanks to NSERC Smart Microgrid Network for their funding that made this possible.
I would also acknowledge everyone from the Department of Electrical and Computer
Engineering for their valuable help during my stay here. I thank all my lab mates for
providing me with a peaceful and friendly environment during these two years. A special
thanks to Bharatkumar Solanki and Indrajit Das for all the suggestions and guidance. We
had some of the greatest discussions that I would always remember. This work would not
have been completed if not for the support of Isha Sharma and Felipe Ramos. I would also
like to thank Ehsan and Benham for their cooperation.
A very special thanks to Srinivasan for you have been an invaluable friend and mentor
to me. Thanks for answering all my questions and introducing me to optimization and
computation. My stay at Waterloo has been great fun, the full credits for which goes to
Swati. I would also like to wholeheartedly thank my friends Mani, Preetam, Rana, Ankita
and Guru. You guys have made each day in waterloo memorable.
Finally, I would like to reserve my special thanks to my family. Your love and belief in
me has made me what I am today. I owe everything to you.
iv
Dedication
I dedicate this work to my parents and my sister Preeti.
v
Contents
List of Figures viii
List of Tables x
Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Glossary of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Demand Response . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.2 Demand Response in Microgrids . . . . . . . . . . . . . . . . . . . . 5
1.3 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Background 9
2.1 Demand Response and Microgrids . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Estimation and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4 Unit Commitment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.5 Optimization Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.5.1 Tools and Solvers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
vi
CONTENTS
3 Modeling Framework for Optimal DR of Controllable Loads 25
3.1 Microgrid Operations Model . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1.1 Overall Framework for Optimal DR . . . . . . . . . . . . . . . . . . 25
3.1.2 Microgrid Energy Management System (MEMS) Mathematical Model 26
3.2 Load Profile Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3 Neural Network-Microgrid Integration . . . . . . . . . . . . . . . . . . . . . 37
3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4 Case Studies 39
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.2 Microgrid Test System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.3 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.3.1 Description of Case Studies . . . . . . . . . . . . . . . . . . . . . . 42
4.3.2 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.3.3 Inclusion of Constant Energy Constraint . . . . . . . . . . . . . . . 52
4.4 Computational Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5 Conclusions and Future Work 55
5.1 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Bibliography 58
vii
List of Figures
2.1 Potential peak reduction by customer class [1]. . . . . . . . . . . . . . . . . 11
2.2 Microgrids and their constituents. . . . . . . . . . . . . . . . . . . . . . . . 12
2.3 MLP architecture [31]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4 log sigmoid function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.5 tan hyperbolic function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1 Proposed architecture of the optimal DR framework. . . . . . . . . . . . . 27
3.2 Neural network architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3 Neural network performance plot. . . . . . . . . . . . . . . . . . . . . . . . 34
3.4 Training state plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.5 Actual vs target output. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.6 Model behavior under random input data. . . . . . . . . . . . . . . . . . . 36
3.7 Error histogram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.8 Regression plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.1 Modified microgrid test system. . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2 Temperature and TOU pricing profile. . . . . . . . . . . . . . . . . . . . . 43
4.3 Base load profile of residential customers in a microgrid. . . . . . . . . . . 43
4.4 PV generation profile. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.5 Wind generation profile. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
viii
LIST OF FIGURES
4.6 Cumulative load profile, base load and uncontrollable load in Case 1. . . . 45
4.7 Optimal generation dispatch in Case 1. . . . . . . . . . . . . . . . . . . . . 46
4.8 ESS dispatch during off-peak in Case 1. . . . . . . . . . . . . . . . . . . . . 46
4.9 ESS dispatch during peak in case 1. . . . . . . . . . . . . . . . . . . . . . . 47
4.10 Comparison of controlled and uncontrolled load profile in Case 1 and 2. . . 48
4.11 Effect of P on the system demand for a selected off-peak interval. . . . 49
max
4.12 Effect of P on the system demand for a selected peak interval. . . . . . 49
max
4.13 Optimal generation dispatch in Case 2. . . . . . . . . . . . . . . . . . . . . 50
4.14 A comparison of diesel generator dispatch under Case 1 and 2. . . . . . . . 50
4.15 ESS dispatch during off-peak in Case 2. . . . . . . . . . . . . . . . . . . . . 51
4.16 ESS dispatch during peak in Case 2. . . . . . . . . . . . . . . . . . . . . . 51
4.17 Absolute change in ESS dispatch. . . . . . . . . . . . . . . . . . . . . . . . 52
4.18 Controllable load shift profile. . . . . . . . . . . . . . . . . . . . . . . . . . 54
ix
List of Tables
4.1 Microgrid DER capacity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2 Comparison of costs between Case 1 and Case 2. . . . . . . . . . . . . . . 52
4.3 Energy dispatch comparison between Case 1 and Case 2. . . . . . . . . . . 52
x